This paper presents a vector control direct (FOC) of double fed induction generator intended to control the generated stator powers. This device is intended to be implemented in a variable-speed wind-energy conversion system connected to the grid. In order to control the active and reactive power exchanged between the machine stator and the grid, the rotor is fed by a bi-directional converter. The DFIG is controlled by standard relay controllers. Details of the control strategy and system simulation were performed using Simulink and the results are presented in this here to show the effectiveness of the proposed control strategy.

1. International Journal of Power Electronics and Drive System (IJPEDS)
Vol. 7, No. 3, September 2016, pp. 973~985
ISSN: 2088-8694, DOI: 10.11591/ijpeds.v7i3.11065  973
Journal homepage: http://iaesjournal.com/online/index.php/IJPEDS
Modeling and Control of a Doubly-Fed Induction Generator for
Wind Turbine-Generator Systems
Mouna Lamnadi*, Mourad Trihi*, Badre Bossoufi**, Abdelkader Boulezhar*
* Department of Physics, Theoretical and Applied Physics Laboratory, University Hassan II Ain Cock Faculty of
Sciences, Casablanca, Morocco
** Laboratory of Electrical Engineering and Maintenance, Higher School of Technology, EST-Oujda, University of
Mohammed I, Morocco
Article Info ABSTRACT
Article history:
Received Nov 12, 2015
Revised Apr 12, 2016
Accepted May 13, 2016
This paper presents a vector control direct (FOC) of double fed induction
generator intended to control the generated stator powers. This device is
intended to be implemented in a variable-speed wind-energy conversion
system connected to the grid. In order to control the active and reactive
power exchanged between the machine stator and the grid, the rotor is fed by
a bi-directional converter. The DFIG is controlled by standard relay
controllers. Details of the control strategy and system simulation were
performed using Simulink and the results are presented in this here to show
the effectiveness of the proposed control strategy.
Keyword:
DFIG
Simulation
Vector control direct
Wind energy
Copyright © 2016 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Mouna Lamnadi,
Department of Physics, Theoretical and Applied Physics Laboratory, University Hassan II Ain Cock
Faculty of Sciences,
Km 8 Route d'El Jadida ,B.P 5366 Maarif Casablanca 20100 Morocco.
Email: mouna.lamnadi@gmail.com
1. INTRODUCTION
In the recent years, renewable energy systems have attracted the great interest because conventional
sources of energy are limited and a number of problems associated with their use, like environment pollution,
large grid requirements etc. Governments of the whole world are forced for the alternative energy sources
such as wind power, solar energy and small hydro-electric power [1]
Morocco, as well as globally, being one of largest energy importer in MENA, is making concerted
efforts to reduce its reliance on imported fossil fuels. Renewable energy is an attractive proposition as
Morocco has almost complete dependence on imported energy carriers. In 2012, Morocco spent around
US$10 billion on all energy imports (crude oil and oil products, coal, natural gas and electricity). Annual
electricity consumption in Morocco was 33.5 TWh in 2014, and is steadily increasing at a rate of around
7 percent each year [2, 3].
Morocco has huge wind energy potential due to it 3,500 km coast line and average wind speeds
between 6 and 11 m/s. Regions near the Atlantic coast, such as Essaouira, Tangier and Tetouan (with average
annual average wind speeds between 9.5 and 11 m/s at 40 metres) and Tarfaya, Laayoune, Dakhla, and Taza
(with annual average wind speed between 7.5 and 9.5 m/s at 40 metres) has excellent wind power potential.
According to a study by CDER and GTZ, the total potential for wind power in Morocco is estimated at
around 7,936 TWh per year, which would be equivalent to about 2,600 GW. Morocco’s total installed wind
power capacity at the end of 2015 was an impressive 787MW.

2. IJPEDS ISSN: 2088-8694 
Modeling and Control of a DFIG for Wind Turbine-Generator Systems (Mouna Lamnadi)
974
Wind energy is the current “star” in the field of renewable energy for electrical production. Still, the
power generated by wind turbines over time is characteristically uneven due to the unpredictable nature of
their primary source of power.
In fixed speed wind energy conversion system often uses from squirrel-cage induction generator that
is connected to grid directly. This machine has maximum efficiency at certain speeds. In variable speed
generating systems, at the beginning, squirrel cage induction generator was used with back to back converter.
This converter controls total power passes through the generator and it raises the cost. Today’s, doubly- fed
induction generator (DFIG) is most suitable for variable-speed constant frequency generating [4, 5]. The
Control of DFIG wind turbine systems are traditionally based on either stator flux oriented control (FOC) [6]
or stator voltage oriented control (VOC) [7].
The Doubly-Fed Asynchronous Generator (DFIG) with FOC control is a machine that has excellent
performance and is commonly used in the wind turbine industry. There are many reasons for using an
Doubly-Fed Asynchronous Generator (DFIG) for wind turbine a variable speed, such as reducing efforts on
mechanical parts, noise reduction and the possibility of control of active power and reactive [10].
The wind system using DFIG generator and a "back-to-back" converter that connects the rotor of the
generator and the network has many advantages. One advantage of this structure is that the power converters
used are dimensioned to pass a fraction of the total system power [8-9]. This allows reducing losses in the
power electronics components. The performances and power generation depends not only on the DFIG
generator, but also the manner in which the two parts of "back-to-back" converter are controlled [11].
The power converter machine side is called "Rotor Side Converter RSC" (PWM Inverter 1) and the
converter Grid-side power is called "Grid Side Converter GSC" (PWM Inverter 2). The RSC converter
controls the active power and reactive power produced by the machine. As the GSC converter, it controls the
DC bus voltage and power factor network side (Figure 1).
Figure 1. Architecture of the Control
2. WIND ENERGY SYSTEM
2.1. Modelling of the Wind-Turbine
By applying the theory of momentum and Bernoulli's theorem, we can determine the incident power
(theoretical power) due to wind [12]:
3
...
2
1
vSPincident  (1)
S : the area swept by the pales of the turbine [m2
]
ρ : the density of the air (ρ =1.225kg / m3
at atmospheric pressure).
v : wind speed [m/s ]
Due to various losses in wind energy system, available on the power extracted from the turbine rotor, is lower
than the incident power. The power extracted is expressed by [13]:
3
).,(...
2
1
vCSP pextracted  (2)

3.  ISSN: 2088-8694
IJPEDS Vol. 7, No. 3, September 2016 : 973 – 985
975
Cp (λ, β) is called the power coefficient, which expresses the aerodynamic efficiency of the turbine. It
depends on the ratio λ, which represents the ratio between the speed at the end of the blades and the wind
speed and the angle of orientation of the blades β. The ratio λ can be expressed by the following relation [13]:
v
Rt.
 (3)
The maximum power coefficient Cp was determined by Albert Betz (1920) as follows:
593.0
27
16
),(max
pC (4)
The power factor is intrinsic to the constitution of the wind-turbine and depends on the profiles of the blades.
We can model the power coefficient with a single equation that depends on the speed ratio λ and pitch angle
β for the blade [13]:
 ...
1
..),( 6
1
4321
5
cecc
A
ccC A
c
p 







(5)
c1= 0.5872, c2= 116, c3 = 0.4, c4= 5, c5 = 21, c6= 0.0085
The six coefficients c1, c2, c3, c4, c5 are modified for maximum Cp equal to 0.498 for β = 0°.
With A which depends on λ and β:
3
1
035.0
.08.0
11
 



A
(6)
The Figure 2 shows the curves of the power coefficient as a function of λ for different values of β. A
coefficient of maximum power of 0.498 is obtained for a speed ratio λ which is 8 (λopt). Fixing β and λ
respectively to their optimal values, the wind system provides optimal power [14-15].
Figure 2. Power coefficient as a function of λ and β
Figure 3.Wind-turbine DFIG characteristics

4. IJPEDS ISSN: 2088-8694 
Modeling and Control of a DFIG for Wind Turbine-Generator Systems (Mouna Lamnadi)
976
The aerodynamic torque on the slow Wind-turbine DFIG characteristics axis can be expressed by
Equation 7: [16-17]
t
p
t
eol
al vCS
P
C




1
.).,(...
2
1 3
 (7)
Ωt : Rotational speed of the turbine.
Cal : Torque on the slow axis (turbine side).
The mechanical speed is related to the speed of rotation of the turbine by the coefficient of the
multiplier. The torque on the slow axis is connected to the torque on the fast axis (generator side) by the
multiplier coefficient.
The total inertia J is formed of the reduced inertia of the turbine and the fast axis of the inertia J of the
generator [16]:
g
Tur
J
G
J
J  2
(8)
JTur : Turbine inertia.
Jg : Inertia of the generator.
To determine the evolution of the mechanical speed from Cmec total torque applied to the rotor of the
DFIG, we apply the fundamental equation of dynamics:
mecemarmec
mec
fCCC
dt
d
J 

. (9)
Ωmec : Mechanical speed of DFIG.
Car : Aerodynamic torque on the fast axis of the turbine.
Cem : Electromagnetic torque.
f : Friction.
The above equations are used to prepare the block diagram of the model of turbine (Figure 4).
Figure 4.Wind-turbine model
2.2. Extraction of Maximum Power
In order to capture the maximum power of the incident energy of the wind-turbine, must
continuously adjust the rotational speed of the wind turbine. The optimal mechanical turbine speed
corresponds has λopt and β = 0°. The speed of the DFIG is used as a reference value for a controller
proportional-integral type (PI phase advance). The latter determines the control set point which is the
electromagnetic torque that should be applied to the machine to run the generator at its optimal speed [18].
The torque thus determined by the controller is used as a reference torque of the turbine model
(Figure 5). The system of variation of the angle of orientation of the blades (variation of the angle of
incidence) to change the ratio between the lift and drag. To extract the maximum power (and maintain
constant), the adjusted angle of incidence of the blades to the wind speed [19-20].
The "Pitch Control" is a technique that mechanically adjusts the blade pitch angle to shift the curve
of the power coefficient of the turbine.
However, it is quite expensive and is generally used for wind turbines and high average power. For
our model, the "Stall Control" technique, which is a passive technique that allows a natural aerodynamic stall

5.  ISSN: 2088-8694
IJPEDS Vol. 7, No. 3, September 2016 : 973 – 985
977
(loss of lift when the wind speed becomes more important). Otherwise, β is zero. The synthesis of the PI
controller requires knowledge of the transfer function of our system. This is especially difficult because of
the power coefficient. A simple proportional correction (P) is obtained after testing. Note that β may vary
from 0 ° to 90 ° characterized by saturation.
Figure 5. Block diagram with control of the speed
2.3. Model of the Doubly Fed Induction Generator
For a better representation of the behavior of a doubly fed induction generator, it is necessary to use
a specific model and simple. The two-phase models (d, q) given by the Park transformation is used.
2.3.1. The electrical equations of DFIG
The equations of the stator voltages Vs (d, q) and the rotor Vr (d, q), the dynamic model are
expressed by DFIG [5]:
















rdr
rq
rqrrq
rqr
rd
rdrrd
sds
sq
sqssq
sqs
sd
sdssd
dt
d
IRV
dt
d
IRV
dt
d
IRV
dt
d
IRV






..
..
..
..
(10)

















sq
sr
sr
rq
r
rq
sd
sr
sr
rd
r
rd
sq
rs
sr
sq
s
sq
sd
r
sr
sd
s
sd
LL
M
L
I
LL
M
L
I
LL
M
L
I
L
M
L
I
















.
..
.
.
1
.
..
.
.
1
.
..
.
.
1
.
.
.
.
1
(11)
2.4. The magnetic equations
The following magnetic equations are taken from electrical equations (11):











sqsrrqrrq
sdsrrdrrd
rqsrsqssq
rdsrsdssd
IMIL
IMIL
IMIL
IMIL
..
..
..
..




(12)

6. IJPEDS ISSN: 2088-8694 
Modeling and Control of a DFIG for Wind Turbine-Generator Systems (Mouna Lamnadi)
978
2.5. The mechanical equation
The electromagnetic torque of the DFIG is:
)..( sdrqsqrdem PC   (13)
with:
φs(d,q), φr(d,q) : Stator and rotor two-phase flow in the reference of PARK.
Is(d,q), Ir(d,q) : Stator currents and rotor in the reference of PARK.
Rs, Rr : Stator and rotor resistances.
Ls, Lr : Inductors cyclic stator and rotor.
M: Cyclic mutual inductance.
p : Number of machine pole pairs.
ωs: Pulsations of the stator electrical quantities.
ωr: Pulsations of the rotor electrical quantities.
3. VECTOR CONTROL OF DFIG BY ORIENTATION FLUX ROTOR
In this work we have proposed a vector control law for DFIG based on the orientation flow rotor. In
this respect, it demonstrates the relations between the stator and rotor variables. These relations will allow the
rotor to act on signals to control the exchange of active and reactive power between the rotor of the machine
and the grid.
In this control, the flow rotor is oriented in the direction axis d. Thus, we can write:
0;  rqrrd  (14)
The expression of the flow rotor and the stator then becomes:













0
.
..
.
rq
Msrrd
rq
sr
sr
sq
rds
sr
r
rd
sr
s
sd
IM
I
M
LL
IL
M
L
M
L




(15)
The expression of the electromagnetic torque then becomes:
Mrqsrsqrdem IILLPC ....).(   (16)
From the previous equation, we can derive the equations linking the rotor and stator voltages:

















rq
srr
ss
rds
sr
r
s
rq
sr
sr
rq
sr
rs
r
srs
sq
rq
srr
s
rd
sr
s
rq
sr
r
rs
rss
rd
sr
sr
rd
sr
rssr
sd
V
M
L
IL
M
L
dt
dI
M
LL
I
M
LR
LR
V
V
M
R
V
M
L
I
M
RR
LL
dt
dI
M
LL
I
M
LRLR
V









..
. (17)

















)..(
).(
sqrd
sr
rs
s
rq
sr
sr
rq
sr
rs
r
srs
ss
srr
rq
sdrq
srr
s
rq
sr
r
rs
rss
rd
sr
sr
rd
sr
rssr
s
sr
rd
VI
M
LL
dt
dI
M
LL
I
M
LR
LR
L
M
V
VV
M
R
I
M
RR
LL
dt
dI
M
LL
I
M
LRLR
L
M
V











7.  ISSN: 2088-8694
IJPEDS Vol. 7, No. 3, September 2016 : 973 – 985
979
The vector control the DFIG allows us to express the expressions of active and reactive power as followings:






rqrdrdrqr
rqrqrdrdr
IVIVQ
IVIVP
..
..
(18)
We replace the Vrd and Vrq tensions in Pr and rQ Qr are obtained:






rdrdrr
rdrdrrqrrdrr
IQ
IIRIRP

22
(19)
The power Pr is proportional to the current Irq if the flow is kept constant. We can then write:
rdr
rqrjr
KIQ
KIPP


(20)
The variables references values are defined to control. Thus we have the rotor currents reference.
K
Q
I
K
P
I
r
rd
r
rq
*
*
*
*


(21)
3.1. Current Control
The current control ensures voltage regulation of the DC bus and control power factor of the grid
side. The objective of the control is to maintain the voltage of DC bus constant while absorbing a current to
be sinusoidal as possible, with the possibility of the grid side the power factor adjustment. The grid side
converter is controlled such that the active power and reactive power grid side are written as follows [6]:
qm
dm
IUQ
IUP
.
2
3
.
2
3


(22)
With, Um is the amplitude of the phase voltage.
Applying the mesh law, we obtain the tension of the filter is written in matrix form in the "abc" plan.
dc
n
n
n
rr V
d
d
d
I
I
I
dt
d
L
I
I
I
R
V
V
V
.
3
2
1
3
2
1
3
2
1
3
2
1











































(23)
This gives the differential equation of continuous DC bus:
 221121 )2()2(
1
IddIdd
Cdt
dV
nnnn
dc
dc
 (24)
With:















213
213
3
1
nnn
m
mnmdc
dc
dc
dc
ddd
III
et
IdI
I
dt
dV
C

8. IJPEDS ISSN: 2088-8694 
Modeling and Control of a DFIG for Wind Turbine-Generator Systems (Mouna Lamnadi)
980
The representation status of an inverter in the plan 'abc' is non-linear (variable in time). We use the PARK
transformation plan “dq” to facilitate implantation and extraction of harmonics:
     






















































3
2
1
3
2
1
3
2
1
)(;)(;)(
I
I
I
P
I
I
d
d
d
P
d
d
V
V
V
P
V
V
q
d
n
n
n
nq
nd
q
d

 )(P : Matrix Park
By applying the Park transformation to equation (22) and (23) we find the following relation
 











qnqdnd
dc
dc
drdcnq
q
rqrq
qrdcnd
d
rdrd
IdId
Cdt
dV
ILVd
dt
dI
LIRV
ILVd
dt
dI
LIRV
1
.
.


(25)
The variables references values are defined to control. These are the reference voltages for the inverter.






dqqq
qddd
eUVV
eUVV
*
*
With:














drq
qrd
q
rqrq
d
rdrd
ILe
ILe
and
dt
dI
LIRU
dt
dI
LIRU


The general structure of the flow rotor orientation in a wind system is detailed in the figure below:
Figure 6. General structure of the orientation control the flow rotor applied to a wind system
Multiplier
Shaft
Rotor
Stator
DFIG
Turbine
Grid
C
RectifierInverter
AC
≈
DC
=
AC
≈
DC
=
Park
dq => abc
Park
dq => abc ϴr
Rotor side converter
VrqVrd
* *
1/ω ϕr rq
Pr
*
1/ω ϕr rd
Qr
*
Ird
*
+
-
Ird
Irq
*
+-
Irq
Converter side Grid
VqVd
* *
Id
*
+
-
Id
Iq
*
+-
Iq
UqUd UrqUrd
Vdc
*
+
-
dcV
-1/((3/2)Um)1/((3/2)Um)
Q
*
P
*
R
R
R
sϴ
+
-
mϴ ϴ

9.  ISSN: 2088-8694
IJPEDS Vol. 7, No. 3, September 2016 : 973 – 985
981
3.2. Simulation and Test Performance & Discussion
The following Figure presents the global model of the wind system is simulated in the Matlab/
Simulink/. The model consists: the wind turbine, the doubly fed induction generator (DFIG), two power
converters that connect the rotor to the grid:
Figure 7. Simulation general diagram of the orientation control the flow rotor on Matlab / Simulink
The system parameters are given in the annexes. A random wind profile is applied to the system Figure 6.
Figure 8. Profile of wind speed
Omega_g
beta
Wind speed (m / s)
Tm
Wind Turbine
[Vdc]
[wr]
[Idq_s]
[Idq_r]
[Ic_Grid]
[Ib_Grid]
[Te]
[Vdq_s]
[Ia_Grid]
t1
Vdr
Vqr
Vd_Grid
Vq_Grid
Ia_Grid
Ib_Grid
Ic_Grid
Vdc
Rectifier//Inverter
P_ref
Q_ref
P_Grid
Q_Grid
Id_Grid
Iq_Grid
Idr
Iqr
Vdc
wr
Vdr
Vqr
Vd_Grid
Vq_Grid
Laws of controls
[Id_Grid]
[Iq_Grid]
[Idr]
[Iqr]
[Vdc]
[Vdc]
[wr]
[W]
[Vdr]
[Vqs]
[Vqr]
[P_Grid]
[Q_ref]
[Vds]
[Q_Grid]
[Iqs]
[Ids]
[wr]
[Iqr]
[Idr]
[Ib_Grid]
[Ia_Grid]
[Te]
[Ic_Grid]
[P_ref]
[Tm]
Ids
Iqs
Idr
Iqr
Ia_reseau
Ib_reseau
Ic_reseau
Te
Tm
P_ref
Q_ref
P_reseau
Q_reseau
Vds
Vqs
Vdr
Vqr
w
Vdc
Displays
Tm
Vabc
Vdr
Vqr
wr
Idq_r
Idq_s
Vdq_s
Te
DFIG
beta
Wind speed (m / s)
Vabc
P_ref
Q_ref
P_Grid
Q_Grid
Control
0 10 20 30 40 50
0
2.5
5
7.5
10
12.5
15
Time (ms)
Windspeed(m/s)

10. IJPEDS ISSN: 2088-8694 
Modeling and Control of a DFIG for Wind Turbine-Generator Systems (Mouna Lamnadi)
982
(a) (b)
(c)
Figure 9. (a) Speed of the wind turbine, (b) Power of the turbine, (c) Electromagnetic torque
(a) (b)
(c)
Figure 10. (a) Coffecient power, (b) Lambda (c) Phis
The following two figures show the wave form of the active and reactive power, stator and rotor.
Figure 11. Power reactive stator and rotor
0 500 1000 1500 2000
0
5 00
10 00
Time (ms)
PowerTurbine
0 500 1000 1500 2000
-100
0
100
200
Time (ms)
Electromagnetic
torqueCem
0 200 500 1000 1500 2000
-1
-0.5
0
0.5
Time (ms)
Power
coefficientCp
0 50 0 100 0 150 0 200 0
0
0.2
0.4
0.6
0.8
1
Time (ms)
Phis
0 500 1000 1500 2000
18
19
20
21
Time (ms)
Lambda()
0 50 0 100 0 150 0 200 0
-10 00
-5 00
0
5 00
10 00
Time (ms)
ReactivePower
Qr
Qs

11.  ISSN: 2088-8694
IJPEDS Vol. 7, No. 3, September 2016 : 973 – 985
983
The following figure shows the wave forms of the voltages and stator currents.
(a) (b)
(c)
Figure 12. (a) Stator voltage and current in the plan “abc” (b) Zoom stator voltage in the plan "abc",
(c) Zoom stator current in the plan "abc".
One can notice that the stator voltage is equal to that of the grid, while the currents obtained are
sinusoidal, which implies a clean energy without harmonics supplied or drawn by the DFIG. The current and
stator voltage are in phase opposition, this means that the stator active power is supplied from the generator
to the grid.
Tthe results obtained for this application, where the following observations can be distinguished:
a. The specific speed l and the power coefficient Cp does not change a lot of values, they are almost
equal to their optimal values references 9 and 0.4999 successively;
b. The wind power captured follows its optimal reference and has the same shape as the wind profile
applied, this rate is also consistent with the wind torque side of the DFIG;
c. The speed of the DFIG is the image of wind causing the wind, it properly follows its reference;
d. The shapes of the electromagnetic torque of the DFIG and its reference, are virtually identical, but
different from the shape of the profile of the wind speed due to the dynamic torque due to inertia;
e. The phase shift between the voltage 180° and the stator current phase reflects a production of active
power only to the stator as illustrated in figure powers;
f. The shape of the components of the stator flux orientation shows a good flow to ensure vector
control well decoupled from the DFIG.
4. CONCLUSION
The object of this work consists in modeling, control and simulation of a wind system operating at
random wind (speed).The application of vector control orientation of the flow rotor gives a simple
stabilization of the wind system.
The results show that the proposal of the rotor flux orientation control system to DFIG of variable
speed can be considered as an interesting solution in field of wind energy. In this respect , this work can be
continued and completed by the implementation of this command in a DSP card.
0 50 0 100 0 150 0 200 0
-200
-100
0
100
200
Time (ms)
VoltagesVs
andCurrentsIS
Is
Vs
0 50 1 00 1 50 2 00
-200
-100
0
100
200
Time (ms)
VoltagesVs
VsA
VsB
VsC
0 50 1 00 1 50 2 00
-150
-100
-50
0
50
100
150
Time (ms)
CurrentsIs
Is
A
Is
B
Is
C

12. IJPEDS ISSN: 2088-8694 
Modeling and Control of a DFIG for Wind Turbine-Generator Systems (Mouna Lamnadi)
984
5. ANNEXE
Table 1. Parametres of wind power system
Parameters of the turbine
Diameter of blade R=35.25 m
Gain multiplier G=16
Inertia of the turbine J=0.3125 Kg.m2
Coefficient of viscosity f=0.00673 m.s-1
Parameters of the DFIG
stator resistance Rs=0.455
rotor resistance Rr=0.62
stator inductance Ls=0.084H
rotor inductance Lr=0.081H
Mutual inductance Msr=0.078H
Number of poles P=2
REFERENCES
[1] Lei Y, Mullane A, Lightbody G, Yacamini R,Modeling of the wind turbine with a doubly-fed induction generator
for grid integration studies, IEEE Trans,Energy Conversion, vol. 21, no. 1, pp. 257-264, Mar. 2006.
[2] Kingdom of Morocco National Office of Electricity and Potable Water”, http://www.one.org.ma/
[3] Minister of Energy, Mining, Water and the Environment (Morocco), “Minister of Energy, Mining, Water and the
Environment (Morocco)”.
[4] T.Ackermann and Soder, L. “An Overview of Wind Energy-Status 2002”. Renewable and Sustainable Energy
Reviews, 6(1-2), 67-127 (2002).
[5] T. Burton, D. Sharpe, N. Jenkins and E. Bossanyi, “Wind Energy Handbook.”John Wiley&Sons, Ltd, 2001.
[6] B. Hopfensperger, D.J. Atkinson, and R. Lakin, “Statorflux-oriented control of a doubly-fed induction machine
with and without position encoder”, IEE Proc.-Electr. Power Applications, vol. 147, no. 4, July 2000, pp. 241- 250.
[7] Y. Zhou, P. Bauer, J.A. Ferreira, and J. Pierik, “Control of DFIG Under Unsymmetrical Voltage Dip”, IEEE Power
Electronics Specialists Conference, 2007, pp. 933-938.
[8] D.V.N. Ananth et V.N. Kumar, “Flux Based Sensorless Speed Sensing and Real and Reactive Power Flow
Control with Look-up Table based Maximum Power Point Tracking Technique for Grid Connected Doubly Fed
Induction Generator”, Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 3, no 4, 2015.
[9] A. Boulahia, M. Adel, et H. Benalla, “Predictive Power Control of Grid and Rotor Side converters in Doubly Fed
Induction Generators Based Wind Turbine”, Bulletin of Electrical Engineering and Informatics, vol. 2, no 4, p.
258–264, 2013.
[10] B.Bossoufi, M.Karim, A.Lagrioui, M.Taoussi, M.L.EL Hafyani, “Backstepping control of DFIG Generators for
Wide-Range Variable-Speed Wind Turbines”, IJAAC International Journal of Automation and Control , pp 122-
140, Vol.8 No.2, July 2014.
[11] Badre Bossoufi, Mohammed Karim, Ahmed Lagrioui, Mohammed Taoussi, Aziz Derouich, “Observer
backstepping control of DFIG-Generators for wind turbines variable-speed: FPGA-based implementation”,
Renewable Energy, 81 (2015) 903e917.
[12] A. Davigny, Participation aux services système de fermes éoliennes à vitesse variable intégrant un stockage inertiel
d’énergie, Thèse de Doctorat, USTL Lille (France), 2007.
[13] Bossoufi, B., Karim, M. Ionita, S., Lagrioui, A. “The Optimal Direct Torque Control of a PMSM drive: FPGA-
Based Implementation with Matlab & Simulink Simulation”, Journal of Theoretical and Applied Information
Technology JATIT, Vol. 28 No. 2, pp 63-72, 30 June 2011.
[14] Bossoufi, B., Karim, M. Ionita, S., Lagrioui, A. “DTC CONTROL BASED ARTIFICIAL NEURAL NETWORK
FOR HIGH PERFORMANCE PMSM DRIVE”, Journal of Theoretical and Applied Information Technology
JATIT, Vol. 33 No. 2, pp 165-176, 30 November 2011.
[15] Bossoufi, B., Karim, M. Ionita, S., Lagrioui, A. “Nonlinear Non Adaptive Backstepping with Sliding-Mode Torque
Control Approach for PMSM Motor”, Journal of Journal of Electrical Systems JES, Vol. 8 No. 2, pp 236-248, June
2012.
[16] B.Bossoufi, M.Karim, A.Lagrioui, M.Taoussi, M.L.EL Hafyani “Backstepping control of DFIG Generators for
Wide-Range Variable-Speed Wind Turbines”, IJAAC International Journal of Automation and Control, pp 122-
140, Vol. 8 No. 2, July 2014.
[17] B.Bossoufi, M.Karim, S.Ionita, A.Lagrioui, “Performance Analysis of Direct Torque Control (DTC) for
Synchronous Machine Permanent Magnet (PMSM)”, 2010 IEEE 16th International Symposium for Design and
Technology of Electronics Packages, IEEE-SIITME’2010, art. No. 5649125, pp. 237-242. Pitesti, Romania.
[18] Badre Bossoufi1, Hala Alami Aroussi1, ElMostafa Ziani1, Ahmed Lagrioui2, Aziz Derouich3 “Low Speed
Sensorless Control of DFIG Generators Drive for Wind Turbines System”.

13.  ISSN: 2088-8694
IJPEDS Vol. 7, No. 3, September 2016 : 973 – 985
985
[19] Bossoufi, B., Karim, M. Ionita, S., Lagrioui, A., (2012b) “Low-Speed Sensorless Control of PMSM Motor drive
Using a NONLINEAR Approach BACKSTEPPING Control: FPGA-Based Implementation”, Journal of
Theoretical and Applied Information Technology JATIT, 29 February, Vol. 36 No. 1, pp 154-166.
[20] X. Yu, K. Strunz, “Combined long-term and shortterm access storage for sustainable energy system”, 2004 IEEE
Power Engineering Society General Meeting, vol. 2, pp. 1946-1951, 10 June 2004.